Malware Classification

Malware Classification is the process of assigning a malware sample to a specific malware family. Malware within a family shares similar properties that can be used to create signatures for detection and classification. Signatures can be categorized as static or dynamic based on how they are extracted. A static signature can be based on a byte-code sequence, binary assembly instruction, or an imported Dynamic Link Library (DLL). Dynamic signatures can be based on file system activities, terminal commands, network communications, or function and system call sequences.

Source: Behavioral Malware Classification using Convolutional Recurrent Neural Networks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 101–110 of 146 papers

Title	Date	Tasks	Status
A Comparison of Graph Neural Networks for Malware Classification	Mar 22, 2023	ClassificationGraph Classification	—Unverified
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification	Mar 7, 2021	ClassificationFeature Engineering	—Unverified
Adversarial Perturbations Against Deep Neural Networks for Malware Classification	Jun 14, 2016	BIG-bench Machine LearningClassification	—Unverified
A Hierarchical Convolutional Neural Network for Malware Classification	Sep 30, 2019	ClassificationGeneral Classification	—Unverified
A Malware Classification Survey on Adversarial Attacks and Defences	Dec 15, 2023	Adversarial AttackClassification	—Unverified
A multi-task learning model for malware classification with useful file access pattern from API call sequence	Oct 19, 2016	ClassificationDocument Classification	—Unverified
A Natural Language Processing Approach to Malware Classification	Jul 7, 2023	ClassificationFeature Engineering	—Unverified
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content	Sep 27, 2018	DenoisingDescriptive	—Unverified
A Novel Feature Representation for Malware Classification	Oct 18, 2022	ClassificationDescriptive	—Unverified
A short review on Applications of Deep learning for Cyber security	Dec 15, 2018	Android Malware DetectionBIG-bench Machine Learning	—Unverified

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All datasets Microsoft Malware Classification Challenge Malimg Dataset MaleVis

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MalConv	Accuracy (10-fold)	9,641	—	Unverified
2	TPOT Classifier	Accuracy (5-fold)	98.94	—	Unverified
3	CNN BiLSTM - Reb Sampl	Accuracy (5-fold)	98.2	—	Unverified
4	Ahmadi et al. (2016): ENT, Bytes 1-G, STR, IMG1, IMG2, MD1, MISC, OPC, SEC, REG, DP, API, SYM, MD2 IMG and Opcode N-Grams + Ensemble Learning (XGBoost)	Accuracy (10-fold)	1	—	Unverified
5	HYDRA	Accuracy (10-fold)	1	—	Unverified
6	Zhang et al. (2016): Total lines of each Section, Operation Code Count, API Usage, Special Symbols Count, Asm File Pixel Intensity Feature, Bytes File Block Size Distribution, Bytes File N-Gram + Ensemble Learning (XGBoost)	Accuracy (10-fold)	1	—	Unverified
7	Orthrus	Accuracy (10-fold)	0.99	—	Unverified
8	Opcode-based Shallow CNN	Accuracy (10-fold)	0.99	—	Unverified
9	Hierarchical Convolutional Network	Accuracy (10-fold)	0.99	—	Unverified
10	SEA	Accuracy (10-fold)	0.99	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GA Designed Deep CNN	Accuracy	0.99	—	Unverified
2	Gray-scale IMG CNN	Accuracy (10-fold)	0.98	—	Unverified
3	GRU + SVM	Accuracy	0.85	—	Unverified
4	FFNN + SVM	Accuracy	0.8	—	Unverified
5	CNN + SVM	Accuracy	0.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Levit-MC	Accuracy	96.6	—	Unverified